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Static Hand Gesture Recognition System Using Artificial Neural Networks and Support Vector Machine

Year 2019, Volume: 10 Issue: 2, 561 - 568, 20.06.2019
https://doi.org/10.24012/dumf.569357

Abstract

References

  • [1] T. Cook, R. Cargill, Say Hello to My Little Friend : JavaScript! 2014.
  • [2] X. Chen, H. Guo, G. Wang, and L. Zhang, “Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition,” in IEEE International Conference on Image Processing (ICIP), 2017, pp. 2881–2885.
  • [3] L. Keskin, C., Kıraç, F., Kara, Y. E., Akarun, “Real time hand pose estimation using depth sensors,” Consum. depth cameras Comput. Vis., pp. 119–137, 2013.
  • [4] T. S. Dinh, D. L., Lee, S., Kim, “Hand number gesture recognition using recognized hand parts in depth images,” Multimed. Tools Appl., vol. 75, no. 2, pp. 1333–1348, 2016.
  • [5] M. P. . G. S. . K. K. . P. K., “Multi-sensor system for driver’s hand-gesture recognition,” in 11th IEEE international conference and workshops on automatic face and gesture recognition (FG), 2015, pp. 1–8.
  • [6] J. Vo, D. H., Huynh, H. H., Doan, P. M., Meunier, “Dynamic Gesture Classification for Vietnamese Sign Language Recognition,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 3, pp. 412–420, 2017.
  • [7] M. Ibanez, R., Soria, A., Teyseyre, A., Campo, “Easy gesture recognition for Kinect,” Adv. Eng. Softw., vol. 76, pp. 171–180, 2014.
  • [8] L. Y., “Hand gesture recognition using Kinect,” in IEEE International Conference on Computer Science and Automation Engineering, 2012, pp. 196–199.
  • [9] X. Xie, J., Shen, “Hand posture recognition using kinect,” in International Conference on Virtual Reality and Visualization (ICVRV), 2015, pp. 89–92.
  • [10] H. S. Nguyen, D. D., Le, “Kinect gesture recognition: Svm vs. rvm.,” in Seventh International Conference on Knowledge and Systems Engineering (KSE), 2015, pp. 395–400.
  • [11] Z. Dong, C., Leu, M. C., Yin, “American sign language alphabet recognition using microsoft kinect,” in IEEE conference on computer vision and pattern recognition workshops, 2015, pp. 44–52.
  • [12] R. K. Nagashree, R. N., Michahial, S., Aishwarya, G. N., Azeez, B. H., Jayalakshmi, M. R., Rani, “Hand gesture recognition using support vector machine,” Int. J. Eng. Sci., vol. 4, no. 6, pp. 42–46, 2005.
  • [13] L. Song, L., Hu, R., Xiao, Y., Gong, “Real-Time 3D Hand Gesture Recognition from Depth Image,” in nd International Conference On Systems Engineering and Modeling (ICSEM-13), 2013.
  • [14] M. Hatto, “Acceleration of Pedestrian Detection System using Hardware-Software Co-design.,” Lund University MSc Thesis, 2015.

Recognition of static hand gesture with using ANN and SVM

Year 2019, Volume: 10 Issue: 2, 561 - 568, 20.06.2019
https://doi.org/10.24012/dumf.569357

Abstract

Hand gesture recognition is a relevant study topic for a reason that sometimes we may not be in position to communicate verbally. There is need to design Hand gesture recognition systems in order to help people adopt to nonverbal communication mainly sign language. However, there is no clue to understand the meaning of gesture through the computers directly. So this calls for definitions that generalize models in a computer. That is why the machine-learning approaches are implemented in recognition systems. There are generally two types of hand gestures recognition systems which researches have concentrated on. These include static and dynamic Hand gesture recognition systems. However, in building Hand gesture recognition systems, various machine learning approaches have been used. For implementing the proposed system, MS Kinect depth sensor was used as a hardware. The Kinect depth sensor is composed of an infrared camera. This is an advantage to the systems that are designed basing on the depth sensing because factors like color, clothing and background have less effect on the performance. So Kinect based depth sensor systems have a high accuracy and performance making them relevant and applicable in our daily lives. In this paper, we propose a static hand gesture recognition system in real time using two machine learning methods namely Support Vector Machine and Artificial Neural Networks. We use of the newly launched Microsoft Kinect sensor for image extraction. The sensor helps us to extract the hand images. We implement the system on a Matlab platform for reasons that Matlab is widely used by researchers in different fields and that can handle complex computations. In the training of the model, we collect a hundred depth-based Histogram of Oriented Gradient features per alphabet from the hand gesture images which we trained, tested and validated using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). From this dataset, we can generate the generalized gesture model for each alphabet image. For the proposed system, the classification with ANN proves a higher performance then SVM.

References

  • [1] T. Cook, R. Cargill, Say Hello to My Little Friend : JavaScript! 2014.
  • [2] X. Chen, H. Guo, G. Wang, and L. Zhang, “Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition,” in IEEE International Conference on Image Processing (ICIP), 2017, pp. 2881–2885.
  • [3] L. Keskin, C., Kıraç, F., Kara, Y. E., Akarun, “Real time hand pose estimation using depth sensors,” Consum. depth cameras Comput. Vis., pp. 119–137, 2013.
  • [4] T. S. Dinh, D. L., Lee, S., Kim, “Hand number gesture recognition using recognized hand parts in depth images,” Multimed. Tools Appl., vol. 75, no. 2, pp. 1333–1348, 2016.
  • [5] M. P. . G. S. . K. K. . P. K., “Multi-sensor system for driver’s hand-gesture recognition,” in 11th IEEE international conference and workshops on automatic face and gesture recognition (FG), 2015, pp. 1–8.
  • [6] J. Vo, D. H., Huynh, H. H., Doan, P. M., Meunier, “Dynamic Gesture Classification for Vietnamese Sign Language Recognition,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 3, pp. 412–420, 2017.
  • [7] M. Ibanez, R., Soria, A., Teyseyre, A., Campo, “Easy gesture recognition for Kinect,” Adv. Eng. Softw., vol. 76, pp. 171–180, 2014.
  • [8] L. Y., “Hand gesture recognition using Kinect,” in IEEE International Conference on Computer Science and Automation Engineering, 2012, pp. 196–199.
  • [9] X. Xie, J., Shen, “Hand posture recognition using kinect,” in International Conference on Virtual Reality and Visualization (ICVRV), 2015, pp. 89–92.
  • [10] H. S. Nguyen, D. D., Le, “Kinect gesture recognition: Svm vs. rvm.,” in Seventh International Conference on Knowledge and Systems Engineering (KSE), 2015, pp. 395–400.
  • [11] Z. Dong, C., Leu, M. C., Yin, “American sign language alphabet recognition using microsoft kinect,” in IEEE conference on computer vision and pattern recognition workshops, 2015, pp. 44–52.
  • [12] R. K. Nagashree, R. N., Michahial, S., Aishwarya, G. N., Azeez, B. H., Jayalakshmi, M. R., Rani, “Hand gesture recognition using support vector machine,” Int. J. Eng. Sci., vol. 4, no. 6, pp. 42–46, 2005.
  • [13] L. Song, L., Hu, R., Xiao, Y., Gong, “Real-Time 3D Hand Gesture Recognition from Depth Image,” in nd International Conference On Systems Engineering and Modeling (ICSEM-13), 2013.
  • [14] M. Hatto, “Acceleration of Pedestrian Detection System using Hardware-Software Co-design.,” Lund University MSc Thesis, 2015.
There are 14 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Julius Bamwenda 0000-0002-6549-940X

Mehmet Siraç Özerdem 0000-0002-9368-8902

Publication Date June 20, 2019
Submission Date May 23, 2019
Published in Issue Year 2019 Volume: 10 Issue: 2

Cite

IEEE J. Bamwenda and M. S. Özerdem, “Static Hand Gesture Recognition System Using Artificial Neural Networks and Support Vector Machine”, DUJE, vol. 10, no. 2, pp. 561–568, 2019, doi: 10.24012/dumf.569357.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456